Christopher F. Baum's An Introduction to Stata Programming, Second Edition, is a great reference for anyone who wants to learn Stata programming.Baum assumes readers have some familiarity with Stata, but readers who are new to programming will find the book accessible. He begins by introducing programming concepts and basic tools. More advanced programming tools such as structures and pointers and likelihood-function evaluators using Mata are gradually introduced throughout the book alongside examples.Many of the examples are of particular interest because they arose from frequently asked questions from Stata users. If you want to understand basic Stata programming or want to write your own routines and commands using advanced Stata tools, Baum's book is a great reference.
An Introduction to Survival Analysis Using Stata, Revised Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata?s survival analysis routines.This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth usage guide for Stata's most widely used st commands, and a collection of tips for using Stata to analyze survival data and to present the results. This book develops from first principles the statistical concepts unique to survival data and assumes only a knowledge of basic probability and statistics and a working knowledge of Stata.
Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials, and piecewise), interactions of continuous predictors, categorical predictors, interactions of categorical predictors, and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models, models where time is a continuous predictor, models with time as a categorical predictor, nonlinear models (such as logistic or ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and pwcompare commands. This new edition reflects new and enhanced features added to Stata, most importantly the ability to label statistical output using value labels associated with factor variables. As a result, output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature, emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally, this second edition illustrates other new features, such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you.
Multilevel and Longitudinal Modeling Using Stata, Third Edition, by Sophia Rabe-Hesketh and Anders Skrondal, looks specifically at Stata?s treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are ?mixed? because they allow fixed and random effects, and they are ?generalized? because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables.Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters organized into four parts. The first part reviews the methods of linear regression. The second part provides in-depth coverage of two-level models, the simplest extensions of a linear regression model.Volume II is devoted to generalized linear mixed models for binary, categorical, count, and survival outcomes. The second volume has seven chapters also organized into four parts. The first three parts in volume II cover models for categorical responses, including binary, ordinal, and nominal (a new chapter); models for count data; and models for survival data, including discrete-time and continuous-time (a new chapter) survival responses. The fourth and final part in volume II describes models with nested and crossed-random effects with an emphasis on binary outcomes.
The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis. Snijders and Bosker?s book is an applied, authoritative and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book provides step-by-step coverage of: ? multilevel theories ? ecological fallacies ? the hierarchical linear model ? testing and model specification ? heteroscedasticity ? study designs ? longitudinal data ? multivariate multilevel models ? discrete dependent variables There are also new chapters on: ? missing data ? multilevel modeling and survey weights ? Bayesian and MCMC estimation and latent-class models. This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix. This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis. Tom A.B. Snijders is Professor of Statistics in the Social Sciences at the University of Oxford and Professor of Statistics and Methodology at the University of Groningen. Roel J. Bosker is Professor of Education and Director of GION, Groningen Institute for Educational Research, at the University of Groningen.
Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely.This book:? Covers both MR and SEM, while explaining their relevance to one another? Includes path analysis, confirmatory factor analysis, and latent growth modeling? Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises? Extensive use of figures and tables providing examples and illustrating key concepts and techniquesNew to this edition:? New chapter on mediation, moderation, and common cause? New chapter on the analysis of interactions with latent variables and multilevel SEM? Expanded coverage of advanced SEM techniques in chapters 18 through 22? International case studies and examples? Updated instructor and student online resources
Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website (www.afhayes.com), along with links to download PROCESS. New to This Edition *Chapters on using each type of analysis with multicategorical antecedent variables. *Example analyses using PROCESS v3, with annotated outputs throughout the book. *More tips and advice, including new or revised discussions of formally testing moderation of a mechanism using the index of moderated mediation; effect size in mediation analysis; comparing conditional effects in models with more than one moderator; using R code for visualizing interactions; distinguishing between testing interaction and probing it; and more. *Rewritten Appendix A, which provides the only documentation of PROCESS v3, including 13 new preprogrammed models that combine moderation with serial mediation or parallel and serial mediation. *Appendix B, describing how to create customized models in PROCESS v3 or edit preprogrammed models.
Rebecca M. Warner?s bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. Applied Statistics II: Multivariable and Multivariate Techniques, Third Edition is a core multivariate statistics text based on chapters from the second half of the original book. The text begins with two new chapters: an introduction to the new statistics, and a chapter on handling outliers and missing values. All chapters on statistical control and multivariable or multivariate analyses from the previous edition are retained (with the moderation chapter heavily revised) and new chapters have been added on structural equation modeling, repeated measures, and on additional statistical techniques. Each chapter includes a complete example, and begins by considering the types of research questions that chapter?s technique can answer, progresses to data screening, and provides screen shots of SPSS menu selections and output, and concludes with sample results sections. By-hand computation is used, where possible, to show how elements of the output are related to each other, and to obtain confidence interval and effect size information when SPSS does not provide this. Datasets are available on the accompanying website. Bundle and SaveApplied Statistics II + Applied Statistics I: Basic Bivariate Techniques, Third Edition Bundle Volume I and II ISBN: 978-1-0718-1337-9 An R Companion for Applied Statistics II: Multivariable and Multivariate Techniques + Applied Statistics IIBundle ISBN: 978-1-0718-3618-7
The fundamental values central to the Messier Jr./Glover/Prawitt text include: student engagement a systematic approach and decision making.Student Engagement: The authors believe students are best served by acquiring a strong understanding of the basic concepts that underlie the audit process and how to apply those concepts to various audit and assurance services. The text is accessible to students through straightforward writing and the use of engaging relevant real-world examples illustrations and analogies. The text explicitly encourages students to ?stop and think? at important points in the text to help them apply principles covered and also helps students see the application of concepts in a practical setting through ?practice insight? boxes.A Systematic Approach: The authors first introduce the three underlying concepts of audit risk materiality and evidence then follow with a discussion of audit planning the assessment of control risk and a discussion of the nature timing and extent of evidence necessary to reach the appropriate level of detection risk. These concepts are then applied to each major business process and related account balances using a risk-based approach (in following with the new standards adopted by the various auditing boards).Decision Making: Since much of auditing practice involves the application of auditor judgment the authors focus on critical judgments and decision-making processes. If a student understands these basic concepts and how to apply them to an audit engagement he or she will be more effective in today's dynamic audit environment. The new edition even includes a full advanced module on Professional Judgment.
Written especially for HR professionals and business people, California Employment Law: An Employer's Guide is the essential resource for avoiding the many perils and pitfalls California employers face. Comprehensively updated to address new developments, the 2019 Edition features: new independent contractor test; new harassment training requirements; class-action waivers in arbitration agreements; new rules on national origin discrimination; requirement that employees be paid for minimal preparation and concluding work; clarification of rules regarding salary history inquiries; new NLRB standards for employee conduct policies; requirements for lawful time clock rounding; rules for rest break pay for commissioned and piece-rate employees; and new rules regarding lactation breaks.
Quick answers to questions about 20 Key Employment Laws This book explains, in plain English, the 20 most important federal employment laws that come up in the workplace. You can look up what each law allows and prohibits, which businesses must comply, and how to fulfill record-keeping, posting, and reporting requirements. Each chapter covers one law, including: Americans with Disabilities Act Age Discrimination in Employment Act Fair Labor Standards Act Family and Medical Leave Act Immigration Reform and Control Act Fair Credit Reporting Act Pregnancy Discrimination Act Equal Pay Act Title VII of the Civil Rights Act of 1964 Older Workers Benefit Protection Act, and Uniformed Services Employment and Reemployment Rights Act. The 6th edition is updated to reflect the latest Supreme Court cases, government regulations, and state laws. Every employer and HR professional should keep it close at hand.
HR metrics and organizational people-related data are an invaluable source of information from which to identify trends and patterns in order to make effective business decisions. But HR practitioners often lack the statistical and analytical know-how to fully harness the potential of this data. Predictive HR Analytics provides a clear, accessible framework for understanding and working with people analytics and advanced statistical techniques. Using the statistical package SPSS (with R syntax included), it takes readers step by step through worked examples, showing them how to carry out and interpret analyses of HR data in areas such as employee engagement, performance and turnover. Readers are shown how to use the results to enable them to develop effective evidence-based HR strategies.This second edition has been updated to include the latest material on machine learning, biased algorithms, data protection and GDPR considerations, a new example using survival analyses, and up-to-the-minute screenshots and examples with SPSS version 25. It is supported by a new appendix showing main R coding, and online resources consisting of SPSS and Excel data sets and R syntax with worked case study examples.
